Senior Machine Learning Software Engineer in Applied Physics

NLP PEOPLE

Liverpool

4 days ago

Create job alert

Join our dynamic team at nTop, where we’re revolutionizing the engineering landscape with our innovative technology. We’re seeking a Senior Machine Learning Software Engineer in Applied Physics to contribute to cutting‑edge projects that greatly enhance the efficiency of engineering design and simulations.

In this role, you’ll tackle complex challenges and work collaboratively within a team that prioritizes learning and growth. Your contributions will help us accelerate physics simulations by 100‑1000 times, drastically improving real‑time engineering design and optimization processes.

What You’ll Do

Develop and deploy surrogate models to replace intensive physics simulations, achieving impressive speedups of 100‑1000x.
Integrate current machine learning solutions seamlessly into the nTop technology ecosystem.
Prepare and curate training datasets by annotating simulation outputs and extracting relevant physics features from multiple fidelity sources.
Conduct rigorous validation of models against high‑fidelity simulations and experimental data, including uncertainty quantification.
Document model architectures, assumptions, and limitations clearly for both technical and non‑technical stakeholders.

Required Experience

MS/PhD in Physics, Applied Mathematics, Mechanical/Aerospace Engineering, or a related computational area.
3+ years of experience developing surrogate or reduced‑order models for physical systems.
Significant expertise in deep learning frameworks (such as PyTorch or TensorFlow) applied to scientific problems.
Strong foundational knowledge in numerical methods, PDEs, and computational physics like FEM or CFD.
Proficient in the Python scientific computing stack: NumPy, SciPy, Pandas, and scikit‑learn.
Experience with uncertainty quantification and Bayesian techniques in machine learning models.
Demonstrated capability in handling large‑scale simulation data within high‑performance computing environments.
Hands‑on experience with at least one physics simulation software package (ANSYS, COMSOL, OpenFOAM, etc.).

Preferred Experience

Familiarity with neural operators (such as DeepONet and Fourier Neural Operators) for PDE solving.
Experience with NVIDIA’s ecosystem for physics AI (including Physics Nemo, Omniverse, SimNet).
Knowledge of NVIDIA Domino for machine learning model management and deployment.
Experience in setting up and deploying inference services.
Proficiency in containerization technologies, especially Docker.
A background in aerospace and energy systems is a plus.
GPU programming experience (using CUDA or OpenCL).
Understanding of digital twin architectures and requirements for real‑time simulation.

Compensation

€70.000‑€80.000 annually plus options.

Company

nTop

Senior (5+ years of experience)

Related Jobs

View all jobs

Senior Lead Software Engineer- Data Engineer, Java/Python

Senior RF Data Scientist / Research Engineer

Senior Data Scientist (Applied AI)

Senior Machine Learning Engineer (Large Systems) Cambridge, UK

Senior Machine Learning Engineer (Large Systems) New Bristol, UK; Cambridge, UK; London, UK

Senior Machine Learning Engineer

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Jan 18, 2026

Careers Jobs

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

Jan 13, 2026

Jobs

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Jan 6, 2026

Education Jobs

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.